diff options
Diffstat (limited to 'tensorflow/python/data/ops/dataset_ops.py')
| -rw-r--r-- | tensorflow/python/data/ops/dataset_ops.py | 36 |
1 files changed, 18 insertions, 18 deletions
diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 6cda2a77cc..8ba98cb88d 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -222,7 +222,7 @@ class Dataset(object): Note that if `tensors` contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more - @{tf.constant} operations. For large datasets (> 1 GB), this can waste + `tf.constant` operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If tensors contains one or more large NumPy arrays, consider the alternative described in @{$guide/datasets#consuming_numpy_arrays$this guide}. @@ -241,7 +241,7 @@ class Dataset(object): Note that if `tensors` contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more - @{tf.constant} operations. For large datasets (> 1 GB), this can waste + `tf.constant` operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If tensors contains one or more large NumPy arrays, consider the alternative described in @{$guide/datasets#consuming_numpy_arrays$this guide}. @@ -331,7 +331,7 @@ class Dataset(object): ``` NOTE: The current implementation of `Dataset.from_generator()` uses - @{tf.py_func} and inherits the same constraints. In particular, it + `tf.py_func` and inherits the same constraints. In particular, it requires the `Dataset`- and `Iterator`-related operations to be placed on a device in the same process as the Python program that called `Dataset.from_generator()`. The body of `generator` will not be @@ -641,7 +641,7 @@ class Dataset(object): Defaults to `True`. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. Returns: Dataset: A `Dataset` of strings corresponding to file names. @@ -706,7 +706,7 @@ class Dataset(object): dataset will sample. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. reshuffle_each_iteration: (Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to `True`.) @@ -863,7 +863,7 @@ class Dataset(object): This transformation combines multiple consecutive elements of the input dataset into a single element. - Like @{tf.data.Dataset.batch}, the tensors in the resulting element will + Like `tf.data.Dataset.batch`, the tensors in the resulting element will have an additional outer dimension, which will be `batch_size` (or `N % batch_size` for the last element if `batch_size` does not divide the number of input elements `N` evenly and `drop_remainder` is `False`). If @@ -871,7 +871,7 @@ class Dataset(object): should set the `drop_remainder` argument to `True` to prevent the smaller batch from being produced. - Unlike @{tf.data.Dataset.batch}, the input elements to be batched may have + Unlike `tf.data.Dataset.batch`, the input elements to be batched may have different shapes, and this transformation will pad each component to the respective shape in `padding_shapes`. The `padding_shapes` argument determines the resulting shape for each dimension of each component in an @@ -883,8 +883,8 @@ class Dataset(object): will be padded out to the maximum length of all elements in that dimension. - See also @{tf.contrib.data.dense_to_sparse_batch}, which combines elements - that may have different shapes into a @{tf.SparseTensor}. + See also `tf.contrib.data.dense_to_sparse_batch`, which combines elements + that may have different shapes into a `tf.SparseTensor`. Args: batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of @@ -1039,7 +1039,7 @@ class Dataset(object): elements are produced. `cycle_length` controls the number of input elements that are processed concurrently. If you set `cycle_length` to 1, this transformation will handle one input element at a time, and will produce - identical results = to @{tf.data.Dataset.flat_map}. In general, + identical results = to `tf.data.Dataset.flat_map`. In general, this transformation will apply `map_func` to `cycle_length` input elements, open iterators on the returned `Dataset` objects, and cycle through them producing `block_length` consecutive elements from each iterator, and @@ -1306,7 +1306,7 @@ class _NestedDatasetComponent(object): class _VariantDataset(Dataset): - """A Dataset wrapper around a @{tf.variant}-typed function argument.""" + """A Dataset wrapper around a `tf.variant`-typed function argument.""" def __init__(self, dataset_variant, structure): super(_VariantDataset, self).__init__() @@ -1342,20 +1342,20 @@ class StructuredFunctionWrapper(object): func: A function from a nested structure to another nested structure. transformation_name: Human-readable name of the transformation in which this function is being instantiated, for error messages. - dataset: (Optional.) A @{tf.data.Dataset}. If given, the structure of this + dataset: (Optional.) A `tf.data.Dataset`. If given, the structure of this dataset will be assumed as the structure for `func` arguments; otherwise `input_classes`, `input_shapes`, and `input_types` must be defined. input_classes: (Optional.) A nested structure of `type`. If given, this argument defines the Python types for `func` arguments. - input_shapes: (Optional.) A nested structure of @{tf.TensorShape}. If + input_shapes: (Optional.) A nested structure of `tf.TensorShape`. If given, this argument defines the shapes and structure for `func` arguments. - input_types: (Optional.) A nested structure of @{tf.DType}. If given, this + input_types: (Optional.) A nested structure of `tf.DType`. If given, this argument defines the element types and structure for `func` arguments. add_to_graph: (Optional.) If `True`, the function will be added to the default graph. experimental_nested_dataset_support: (Optional.) If `True`, the function - will support @{tf.data.Dataset} objects as arguments and return values. + will support `tf.data.Dataset` objects as arguments and return values. Raises: ValueError: If an invalid combination of `dataset`, `input_classes`, @@ -1478,7 +1478,7 @@ class StructuredFunctionWrapper(object): self._function._create_definition_if_needed() # pylint: disable=protected-access def _defun_args(self): - """Returns a flat list of @{tf.DType} for the input element structure.""" + """Returns a flat list of `tf.DType` for the input element structure.""" ret = [] for input_type, input_class in zip(nest.flatten(self._input_types), nest.flatten(self._input_classes)): @@ -1523,7 +1523,7 @@ def flat_structure(dataset): `**flat_structure(self)` to the op constructor. Args: - dataset: A @{tf.data.Dataset}. + dataset: A `tf.data.Dataset`. Returns: A dictionary of keyword arguments that can be passed to many Dataset op @@ -1846,7 +1846,7 @@ class ShuffleDataset(Dataset): dataset will sample. seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See - @{tf.set_random_seed} for behavior. + `tf.set_random_seed` for behavior. reshuffle_each_iteration: (Optional.) A boolean, which if true indicates that the dataset should be pseudorandomly reshuffled each time it is iterated over. (Defaults to `True`.) |